COURSE CURRICULUM

The Master of Science (M.S.) in Optimization (OPT) at Lehigh University is designed for students whose professional or research goals include work in educational, governmental, or industrial environments. The program equips graduates with a deep understanding of how to design, analyze, implement, and apply optimization algorithms to solve complex problems across engineering and data science. Students gain both theoretical knowledge and practical experience, preparing them to contribute to research initiatives or tackle real-world challenges in a variety of professional settings.The program requires a total of 30 credit hours, structured to provide both specialized and broad-based training. Students must complete 12 credit hours of graduate coursework focused specifically on optimization, which covers the development and application of algorithms, modeling techniques, and solution strategies. In addition, students take 9 credit hours of broader coursework within the Department of Industrial and Systems Engineering (ISE), allowing them to explore complementary areas such as operations research, statistical modeling, or systems analysis. The remaining 9 credit hours can be drawn from relevant courses offered by any department at Lehigh University, enabling students to tailor their studies to their individual interests and professional objectives.All coursework must be approved by the ISE Master Program Director to ensure a cohesive and rigorous program of study. The degree can include project-based courses or a master’s thesis, offering students the opportunity to engage in hands-on research and apply optimization techniques to practical problems. By completing the M.S. in Optimization, students develop a strong foundation in both the theoretical and applied aspects of optimization, positioning them for successful careers in research, industry, or further academic study. The program’s combination of specialized expertise, interdisciplinary coursework, and research opportunities ensures graduates are well-prepared to address complex challenges in engineering, data science, and related fields.